The nature of statistical learning theory
The nature of statistical learning theory
Pairwise classification and support vector machines
Advances in kernel methods
A hierarchical method for multi-class support vector machines
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Computer Vision and Image Understanding
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Large scale natural image classification by sparsity exploration
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
Exploratory undersampling for class-imbalance learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
A comparison of different off-centered entropies to deal with class imbalance for decision trees
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A fast dual method for HIK SVM learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
What does classifying more than 10,000 image categories tell us?
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Image classification using super-vector coding of local image descriptors
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Product Quantization for Nearest Neighbor Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Additive Kernels via Explicit Feature Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-scale image classification: Fast feature extraction and SVM training
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
High-dimensional signature compression for large-scale image classification
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Power mean SVM for large scale visual classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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The usual frameworks for visual classification involve three steps: extracting features, building codebook and encoding features, and training classifiers. The current release of ImageNet dataset [1] with more than 14M images and 21K classes makes the problem of visual classification become more difficult to deal with. One of the most difficult tasks is to train a fast and accurate classifier. In this paper, we address this challenge by extending the state-of-the-art large scale classifier Power Mean SVM (PmSVM) proposed by Jianxin Wu [2] in two ways: (1) The first one is to build the balanced bagging classifiers with under-sampling strategy. Our algorithm avoids training on full data and the training process of PmSVM rapidly converges to the optimal solution, (2) The second one is to parallelize the training process of all classifiers with multi-core computers. We have developed the parallel versions of PmSVM based on high performance computing models. The evaluation on 1000 classes of ImageNet (ILSVRC 1000 [3]) shows that our approach is 90 times faster than the original implementation of PmSVM and 240 times faster than the state-of-the-art linear classifier (LIBLINEAR [4]).